Why the study?
Prediction of readmission and death following hospitalization for heart failure remains an unmet clinical need.
Does the degree of predischarge pulmonary congestion assessed by noninvasive lung impedance predict time to readmission and time to death in patients hospitalized for worsening heart failure?
Does the degree of predischarge pulmonary congestion assessed by noninvasive lung impedance predict time to readmission and time to death in patients hospitalized for worsening heart failure?
Answers are grounded in this paper and cite their sources.
Predischarge pulmonary congestion assessed by noninvasive lung impedance is a strong independent predictor of readmission and mortality in patients hospitalized for worsening heart failure.
BACKGROUND: Prediction of readmission and death after hospitalization for heart failure (HF) is an unmet need. AIM: We evaluated the ability of clinical parameters, NT-proBNP level and noninvasive lung impedance (LI), to predict time to readmission (TTR) and time to death (TTD). METHODS AND RESULTS: The present study is a post hoc analysis of the IMPEDANCE-HF extended trial comprising 290 patients with LVEF ≤45% and New York Heart Association functional class II-IV, randomized 1:1 to LI-guided or conventional therapy. Of all patients, 206 were admitted 766 times for HF during a follow-up of 57 ± 39 months. The normal LI (NLI), representing the "dry" lung status, was calculated for each patient at study entry. The current degree of pulmonary congestion (PC) compared with its dry status was represented by ΔLIR = ([measured LI/NLI] - 1) × 100%. Twenty-six parameters recorded during HF admission were used to predict TTR and TTD. To determine the parameter which mainly impacted TTR and TTD, variables were standardized, and effect size (ES) was calculated. Multivariate analysis by the Andersen-Gill model demonstrated that ΔLIRadmission (ES = 0.72), ΔLIRdischarge (ES = -3.14), group assignment (ES = 0.2), maximal troponin during HF admission (ES = 0.19), LVEF related to admission (ES = -0.22) and arterial hypertension (ES = 0.12) are independent predictors of TTR (p < 0.01, χ2 = 1,206). Analysis of ES showed that residual PC assessed by ∆LIRdischarge was the most prominent predictor of TTR. One percent improvement in predischarge PC, assessed by ∆LIRdischarge, was associated with a likelihood of TTR increase by 14% (hazard ratio [HR] 1.14, 95% confidence interval [CI] 1.13-1.15, p < 0.01) and TTD increase by 8% (HR 1.08, 95% CI 1.07-1.09, p < 0.01). CONCLUSION: The degree of predischarge PC assessed by ∆LIR is the most dominant predictor of TTR and TTD.
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Kleiner‐Shochat et al. (2020) studied this question.
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